# Evaluate a Kerasnip Model This function provides an `kera_evaluate()` method for `model_fit` objects created by `kerasnip`. It preprocesses the new data into the format expected by Keras and then calls `keras3::evaluate()` on the underlying model to compute the loss and any other metrics. ```r keras_evaluate(object, x, y = NULL, ...) ``` ## Arguments - `object`: A `model_fit` object produced by a `kerasnip` specification. - `x`: A data frame or matrix of new predictor data. - `y`: A vector or data frame of new outcome data corresponding to `x`. - `...`: Additional arguments passed on to `keras3::evaluate()` (e.g., `batch_size`). ## Returns A named list containing the evaluation results (e.g., `loss`, `accuracy`). The names are determined by the metrics the model was compiled with. ## Details Evaluate a Fitted Kerasnip Model on New Data ## Examples ```r if (requireNamespace("keras3", quietly = TRUE)) { library(keras3) library(parsnip) # 1. Define layer blocks input_block <- function(model, input_shape) { keras_model_sequential(input_shape = input_shape) } hidden_block <- function(model, units = 32) { model |> layer_dense(units = units, activation = "relu") } output_block <- function(model, num_classes) { model |> layer_dense(units = num_classes, activation = "softmax") } # 2. Define and fit a model ---- create_keras_sequential_spec( model_name = "my_mlp_tools", layer_blocks = list( input = input_block, hidden = hidden_block, output = output_block ), mode = "classification" ) mlp_spec <- my_mlp_tools( hidden_units = 32, compile_loss = "categorical_crossentropy", compile_optimizer = "adam", compile_metrics = "accuracy", fit_epochs = 5 ) |> set_engine("keras") x_train <- matrix(rnorm(100 * 10), ncol = 10) y_train <- factor(sample(0:1, 100, replace = TRUE)) train_df <- data.frame(x = I(x_train), y = y_train) fitted_mlp <- fit(mlp_spec, y ~ x, data = train_df) # 3. Evaluate the model on new data ---- x_test <- matrix(rnorm(50 * 10), ncol = 10) y_test <- factor(sample(0:1, 50, replace = TRUE)) eval_metrics <- keras_evaluate(fitted_mlp, x_test, y_test) print(eval_metrics) # 4. Extract the Keras model object ---- keras_model <- extract_keras_model(fitted_mlp) summary(keras_model) # 5. Extract the training history ---- history <- extract_keras_history(fitted_mlp) plot(history) remove_keras_spec("my_mlp_tools") } ```

keras_evaluate function